Size Has No Impact On Faceoff Percentage: 2009-10 Data

A question that has come up a few times is whether big players tend to have an advantage when taking faceoffs. There is a certain logic to the idea that they do: after all, bigger, stronger players should be able to outmuscle their smaller counterparts in the faceoff circle.

The data, however, suggests something else entirely: that there is no advantage to being big when it comes to taking face-offs.

I went back to the 2009-10 season, and took every NHL centre who had taken 100+ even-strength face-offs. Then I plotted three charts, one comparing even-strength faceoffs to height, another to weight, and a third to “size” – just the product of height and weight. I used even-strength faceoffs only to make for a fair comparison, as it’s easier to win faceoffs while on the man advantage and harder while killing penalties.

Face-offs vs. Height

Correlation: -0.02 (Scale of -1 to 1)

Face-offs vs. Weight

Correlation: 0.03 (Scale of -1 to 1)

Face-offs vs. Size

Correlation: 0.01 (Scale of -1 to 1)

Conclusion

There is no noticeable advantage or disadvantage granted by size in the faceoff circle. The best faceoff men in the game last season varied from small (Scott Nichol, Kyle Wellwood, Vladimir Sobotka, Todd Marchant) to large (David Steckel, Wayne Primeau, Paul Gaustad, Vincent Lecavalier) and the worst faceoff men varied from small (Oscar Moller, Andrew Cogliano, Daymond Langkow, Toby Petersen) to large (Brian Boyle, Eric Staal, Michael Rupp, Nik Antropov).

It’s one of the few areas in the game where the playing field is relatively level.

The top ten are bIg relative to the size of the average guy on the street, but the top ten and just slightly above avg in terms of NHLers. They certainly do nothing to discredit the argument that JW just put up.

These are the attributes of the analysis presented by Jonathan that make its conclusions reliable, reputable, and accurate:

1. A clear and unambiguous test question.
(We actually have a 3-part test question.)
–> How much do differences in a player’s height, weight, or size make to face-off success?
[The question is NOT: “How heavy or tall are the people who get the best FO%?”; nor, “How can we explain this year’s FO leaders?”, nor “How does age affect FOs?, nor “What about a playoff team vs a lottery team?”.]

2. Clearly stated assumptions, definitions, and limitations.
(These serve to eliminate potential ambiguities and to make sure the question can be tested saliently.)
–> height is in inches, weight is in pounds, size is in inches*pounds/100
–> The data for the analysis is all 09/10 regular season EV FOs taken by players with 100+ such FOs.
[That means the analysis is not about power plays or players who infrequently take draws. It also means that it is covering players most likely to be the coaches’ favorites for taking draws. That is a way of reducing the effect other variables might accidentally colour this analysis. Most importantly: when this analysis is complete we ought to be able to say something about whether size matters to players taking most of the draws, not just the best ones, nor the ones who are considered to poor to be sent on draws very often.]

3. The sample size is statistically large enough that, if there is a correlation, it will be noticed. Conversely, uncorrelated means uncorrelated, with no ambiguity.
–> The only way I can think of to describe adequacy of sample size is to ask that the reader imagine removing half of the dots (at random) from the scatter charts. Would the nearly horizontal slope line need to be tipped one way or the other as a result. Nope – there are no ambiguities created by not looking at enough data.
[This is actually why Jonathan is very safe in his prediction of 2010 results — the sample size of his analysis is great enough that even if half the players are missing, or if another bunch of NHLers is analyzed, the conclusions will not change.]

4. The analysis method remains true to the test question and the assumptions.
–> Just plot the data on a simple graph. Voila.

5. The conclusion is a direct answer to the test question, with no intervention on the part of the writer.
–> “There is no noticeable advantage or disadvantage granted by size in the faceoff circle. . . It’s one of the few areas in the game where the playing field is relatively level.

**** So with that in mind – can we all STOP worrying about how BIG Cogs and Gags are and somehow find a way to teach a very important skill that lots of players possess.

This is going to sound stupid, but: The very best correlation that exists for future FO% success is past FO% success. Some guys just suck. Maybe they should never be allowed near the dot until they learn. But its not cuz they’re small.

It is for these reasons that I strongly doubt Robin’s statements that things are different this year. The top 10 are a very small sample size. As we’ve seen in later posts, it wouldn’t be difficult to find years where the top 10 are small. Whereas I’m sure the data will suggest that there is no correlation, year after year.

Unless we have some reason to believe that something fundamental has changed about faceoffs this year that would be creating this advantage. I’m no expert, but I daresay we don’t.

I’m an Electrical Engineer. I have 5 years of university plus 20 years experience as a manager who regularly parses between good analysis, incomplete analysis, and crap. I have studied statistics in the past and I know about numbers, generally.

The problems with Robin’s statements do not merely lie in the small sample size of 10, they are that Robin, and some others, keep moving the issue around, and keep disallowing various contrary data points by altering the question, the constraints, the method, and their original conclusions.

This happens over and over again, regarding issues such as faceoffs, fighting, “intangibles” (Strudwick), goalies, and so on. I am positive that the mistakes are not intentional, so I tried to describe Jonathan’s analysis in layman’s terms. Hopefully we can come to understand that one reason statistical analysis is performed is that it takes the bias and other human limitations out of the issue.

If people believe that they are being lied to with statistics, it ought to mean that they have spotted a relevant fallacy in the analysis performed. That possibility can be tested and conclusions based on the new test can be made. For example, someone can tell you that global ocean temperatures are 1 deg. C more than in 1970. And, that humans are creating 3x as much CO2 each year since 1970. Those numbers are based on statistical analysis — no one was “there” measuring “everything”. To know whether they are true, we would have to learn exactly how the numbers were arrived at, or have a deep seated trust in the people telling us the information. The conclusion that could come up next is “therefore we need to cut CO2 output in Alberta by 30% (or whatever) or we’re all going to die and the people in Zambia will be very upset so we better start shipping our money to the government by the trainload”. The lie is less likely to be in the statistics and more likely to be in the interpretation or untested conclusions.

I’m not a statistician, but a scatter plot does not seem to be the best method of analysis. It seems a regression analysis would be more suitable. That will allow you to demonstrate any corrolation between height, weight and face off %. Instead of each one independently.

The two bivariate scatterplots are fine. If there is no correlation between height/wins or weight/wins, then clearly a multivariate regression of all three variables is still going to show no correlation.

If there was even a weak correlation, it might be worth it to see how the two independent variables combined correlate to wins, but there isn’t.

@JW – I appreciate the work that you put into posts like this and find them interesting. Thanks.

@Shingin – Along the lines of your suggestion, I would love to see someone look at whether centermen can show improvement over time and if so, how long does it take (ie: are Cogs/Fraser/Gagner etc at all likely to improve on the dot, or is what you see what you get – for eternity.)

I know that this would be a back-breaker to compile, but in terms of the Oilers situation, I think that this would be highly relevant with regards to projected future value of these players.

This is good stuff JW, and I bet you’re right, but I have to be mr. poopy pants for a second:

I believe that stats are better used to summarize experimental results than to draw conclusions from arbitrary samples. The problem with your conclusion here is that its not necessarily correlative to your results due to the sample group having inherent variables.

Based on what you’ve shown, the statement “size has no bearing on face-off ability” could just as easily be replaced by “to be an undersized NHL player taking faceoffs, you have to show exemplary skill compared to other little guys, who don’t make the show”.

I would go so far as to say that you should look at players who take less than 20 faceoffs per year and see if size matters. Does size favor the “its my first draw” crowd?

Assuming the NHL measures faceoff wins by who gets possession of the puck following a draw then don’t you think the bulk of faceoff wins would come from the bigger wingers who are able to fend off their opponent and get the puck back to their team.

Maybe that’s why Horcoff’s percentage has gone down as his wingers have gotten smaller… Is there any way to look into that?